An Enhanced Multi-Factor Device Authentication Protocol in IoLT Healthcare Environment
Bibliographic record
Abstract
Mobile sequencing enables a rapid process of determining the order of nucleotides in deoxyribonucleic acid (DNA). The process is carried out by portable sequencers, which are the main element in the internet of living things (IoLT). This approach assists in obtaining rapid biological insights at the source regardless of the patient's geographical location, for efficient care therapies as well as scientific discovery. Sequencing data and/or related analytical results produced in various formats will be sent from the sequencer to medical experts or healthcare professionals for performing the services. Communication in such IoLT environments encounters certain security concerns regarding information confidentiality and data integrity. Recently, Ren et al. proposed an anonymous user authentication scheme securing IoT communications, which is applicable to the IoLT. However, we found their work has some serious security issues, e.g., it is vulnerable to man-in-the-middle attacks, stolen-device attacks, etc. This paper proposes an enhanced multi-factor device authentication (MFDA) protocol to address all weaknesses of Ren et al.’ s work. In addition to the inherent device-to-cloud communication function, some other novel properties are supported in the MFDA, including group-oriented device-to-device communication, password and biometrics alteration, device revocation, and regrouping function. Security and performance evaluation shows that our protocol is robust against various attacks with a rational implementation cost. The proposed work paves a new way for future research ideas that further discover IoLT applications in the healthcare sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".